Eduardo F. Morales

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In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do(More)
Many vision systems use skin detection as a principal component. Skin detection algorithms, normally evaluate a single and thus limited color model, such as HSV, Y CrCb, YUV, RGB, normalized RGB, etc. Their limited performance, however, suggests that they are looking at the incorrect color models. This paper describes a new constructive induction algorithm(More)
Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to provide domain knowledge with extra rewards to converge faster. The reward shaping functions are normally defined in advance by the user and are static. This paper introduces a(More)
Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficult to solve with existing reinforcement learning methods due to the large search space. In this paper, we use a relational representation to define powerful abstractions that allow(More)
It has been argued that much of human intelligence can be viewed as the process of matching stored patterns. In particular, it is believed that chess masters use a pattern–based knowledge to analyze a position, followed by a pattern–based controlled search to verify or correct the analysis. In this paper, a first–order system, called PAL, that can learn(More)
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but(More)
ion and Refinement for Solving Continuous Markov Decision Processes Alberto Reyesand Pablo Ibargüengoytia Inst. de Inv. Eléctricas Av. Reforma 113, Palmira, Cuernavaca, Mor., México {areyes,pibar}@iie.org,mx L. Enrique Sucar and Eduardo Morales INAOE Luis Enrique Erro 1, Sta. Ma. Tonantzintla, Pue., México {esucar,emorales}@inaoep.mx